Machine Learning-Based Angle of Arrival Estimation for Ultra-Wide Band Radios

Naseri, Mostafa; Shahid, Adnan; Gordebeke, Gert-Jan; Boes, Michiel; Velde, Samuel Van de; Poorter, Eli De

doi:10.1109/lcomm.2022.3167020

Cited by 16 publications

(13 citation statements)

References 19 publications

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“…The overall achieved position estimate had a Root-Mean-Squared (RMS) error of 0.37 m and 3.6°. Instead of classifying the CIR into different bins, [28] applied a deep CNN to perform a regression-based AoA estimation using an N CIR of 5. The ML-based solution outperforms the classical PDoA approach in the range of ±90 • , mainly due to the non-linearity PDoA suffers from around extreme angles.…”

Section: Related Workmentioning

confidence: 99%

Angle of Arrival and Centimeter Distance Estimation on a Smart UWB Sensor Node

Margiani,

Cortesi,

Keller

et al. 2023

IEEE Trans. Instrum. Meas.

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Accurate and low-power indoor localization is becoming more and more of a necessity to empower novel consumer and industrial applications. In this field, the most promising technology is based on Ultra-Wideband (UWB) modulation; however, current UWB positioning systems do not reach centimeter accuracy in general deployments due to multi-path and nonisotropic antennas, still necessitating several fixed anchors to estimate an object's position in space. This paper presents an in-depth study and assessment of Angle of Arrival (AoA) UWB measurements using a compact, low-power solution integrating a novel commercial module with Phase Difference of Arrival (PDoA) estimation as integrated feature. Results demonstrate the possibility of reaching centimeter distance precision and 2.4°average angular accuracy in many operative conditions, e.g., in a 90°range around the center. Moreover, integrating the channel impulse response, the phase differential of arrival, and the point-to-point distance, an error correction model is discussed to compensate for reflections, multi-paths, and frontback ambiguity.

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Section: Related Workmentioning

confidence: 99%

Angle of Arrival and Centimeter Distance Estimation on a Smart UWB Sensor Node

Margiani,

Cortesi,

Keller

et al. 2023

IEEE Trans. Instrum. Meas.

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“…We use the signal model introduced in this section to obtain training-and label data for our approach. Please note that our task is similar to the DoA estimation tackled by the previous works [1][2][3][4][5][6].…”

Section: Signal Modelmentioning

confidence: 99%

“…Until recently, available solutions for the task could be loosely divided into three groups: subspace algorithms, iterative maximum likelihood (ML), and Sparse Signal Recovery (SSR). Recent publications [1][2][3][4][5][6] added a fourth category: Deep Neural Networks (DNNs)-based algorithms.…”

Section: Introductionmentioning

confidence: 99%

Grid-free Harmonic Retrieval and Model Order Selection using Deep Convolutional Neural Networks

Schieler¹,

Semper²,

Faramarzahangari³

et al. 2022

Preprint

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This paper introduces a Deep Learning approach for signal parameter estimation in the context of wireless channel modeling. Our work is capable of multidimensional parameter estimation from a signal containing an unknown number of paths. The signal parameters are estimated relative to a predefined grid, providing quasi gridfree, hence, more accurate estimates than previous grid-limited approaches. It requires no prior knowledge of the number of paths, giving it an advantage in terms of complexity compared to existing solutions. Along with the description, we provide an initial performance analysis and a comparison with State-of-the-Art techniques and discuss future research directions.

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“…The second approach is to find the angle of the received signal on the receiving antenna. Using that angle, the angle of arrival (AoA) technique helps in finding out the location of the end node [10][11] [12]. The third approach can be the time-based approach.…”

Section: Introductionmentioning

confidence: 99%

Support Vector Regression based Localization Approach using LoRaWAN

Magsi¹,

Khir²,

Nawi³

et al. 2023

IJACSA

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The Internet of Things (IoT) domain has experienced significant growth in recent times. There has been extensive research conducted in various areas of IoT, including localization. Localization of Long Range (LoRa) nodes in outdoor environments is an important task for various applications, including asset tracking and precision agriculture. In this research article, a localization approach using Support Vector Regression (SVR) has been implemented to predict the location of the end node using LoRaWAN. The experiments are conducted in the outdoor campus environment. The SVR used the Received Signal Strength Indicator (RSSI) fingerprints to locate the end nodes. The results show that the proposed method can locate the end node with a minimum error of 36.26 meters and a mean error of 171.59 meters.

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Machine Learning-Based Angle of Arrival Estimation for Ultra-Wide Band Radios

Cited by 16 publications

References 19 publications

Angle of Arrival and Centimeter Distance Estimation on a Smart UWB Sensor Node

Angle of Arrival and Centimeter Distance Estimation on a Smart UWB Sensor Node

Grid-free Harmonic Retrieval and Model Order Selection using Deep Convolutional Neural Networks

Support Vector Regression based Localization Approach using LoRaWAN

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